Statistics 150, Stochastic Processes, Spring 2008

Instructor: Steven N. Evans, 329 Evans Hall, evans "at" stat.berkeley.edu

Class time and place: Mon + Wed, 1:30-3:00pm, 334 Evans

 

 

Wikipedia gives the following nice definition of the term stochastic process:

 

 “…  a stochastic process can be thought of as a random function. In practical applications, the domain over which the function is defined is a time interval (time series) or a region of space (random field).

 

Familiar examples of time series include stock market and exchange rate fluctuations, signals such as speech, audio and video; medical data such as a patient's EKG, EEG, blood pressure or temperature; and random movement such as Brownian motion or random walks.

 

Examples of random fields include static images, random topographies (landscapes), or composition variations of an inhomogeneous material.”

 

Some of the topics I will cover in the course are:

 

 

 

GSI: Peter Ralph, plr "at" stat.berkeley.edu

 

Office hours for Steve Evans: Mon 4:00-5:00 and Wed 4:00-5:00.

 

Grading:  Homework 40%, mid-term (in class, March 5) 20%,  final exam 40%


Homework: Homework will be due in class each Wednesday.  I will post the homework to the bSpace site for the class the previous Wednesday.  The class will be divided into groups of approximately 5 students, and each group will work together to turn in a single set of answers.  The answers must be typeset in LaTeX and turned in in paper form: e-mails of homework will not be accepted.  You can read about LaTeX at

http://en.wikipedia.org/wiki/LaTeX

It is available on the Stat department computer system and you can get free distributions for your own computers from MiKTeX (Windows) and MacTeX (Mac OS X) (links on the above web pages).

Good tutorials about LaTeX are
http://ctan.tug.org/tex-archive/info/lshort/english/lshort.pdf
http://www.stat.berkeley.edu/~spector/latex2e.pdf
http://bibserver.berkeley.edu/205/latex_intro.html

See also
http://www.andy-roberts.net/misc/latex/
for an extensive list of tutorials on special topics.



 

Prerequisites: Students should be VERY comfortable with the machinery of elementary probability theory as covered in Statistics 134 (students who received a grade lower than B- will probably have a tough time). 

Text:    Probability and Random Processes by Geoffrey Grimmett and David Stirzaker (Oxford). The “slides” that I will project from my laptop are available here.  NOTE: The slides are just the framework of what we will discuss in class.  They are  certainly NOT a substitute for attending class.

 

Recommended reading: Stochastic Processes by Sheldon Ross (Wiley) and A First Course in Stochastic Processes by Samuel Karlin and Howard M. Taylor (Academic Press).